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This paper introduces a Multi-Strategy Improved Black Widow OptimizationAlgorithm (MSBWOA), designed to enhance the performance of the standard BlackWidow Algorithm (BW) in solving complex optimization problems. The proposedalgorithm integrates four key strategies: initializing the population usingTent chaotic mapping to enhance diversity and initial exploratory capability;implementing mutation optimization on the least fit individuals to maintaindynamic population and prevent premature convergence; incorporating anon-linear inertia weight to balance global exploration and local exploitation;and adding a random perturbation strategy to enhance the algorithm's ability toescape local optima. Evaluated through a series of standard test functions, theMSBWOA demonstrates significant performance improvements in various dimensions,particularly in convergence speed and solution quality. Experimental resultsshow that compared to the traditional BW algorithm and other existingoptimization methods, the MSBWOA exhibits better stability and efficiency inhandling a variety of optimization problems. These findings validate theeffectiveness of the proposed strategies and offer a new solution approach forcomplex optimization challenges.

Enhancing Optimization Through Innovation: The Multi-Strategy Improved Black Widow Optimization Algorithm (MSBWOA)